Ensuring Privacy in Digital Twin Ecosystems

Protect your organization’s virtual replicas from escalating cyber threats with this definitive guide to embedding privacy-by-design strategies and advanced encryption techniques into the core of your digital twin ecosystems.

Digital twins, which create virtual replicas of physical entities, enable real-time monitoring, predictive analytics, and optimized operations, revolutionizing the way organizations approach efficiency, decision-making, and innovation. However, with the growing reliance on digital twins comes an escalating concern about privacy and data security. These ecosystems collect vast amounts of sensitive data, making them prime targets for cyber threats and privacy breaches. This book focuses on the intricate challenges and solutions related to maintaining privacy and data security in digital twin environments. It provides comprehensive insights into the security frameworks, technologies, and best practices that ensure the protection of digital twin environments, safeguarding them from breaches, misuse, and unauthorized access. The content will delve into a variety of topics, including privacy-by-design strategies, encryption techniques, data anonymization methods, and AI-driven privacy solutions specifically tailored for digital twin ecosystems. Case studies will be explored to highlight real-world examples of privacy vulnerabilities and how organizations overcame these challenges. By integrating technical discussions with legal and ethical frameworks, the book offers a holistic approach that caters to both practitioners and researchers at an intermediate-to-advanced level.

Shubham Mahajan, PhD is a Visiting Assistant Professor in the Department of Communication and Computer Engineering at Al-Ahliyya Amman University in Jordan. He has published more than 70 articles in peer-reviewed journals and conferences, as well as 11 Indian, one Australian, and one German patents. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication.

Davinder Paul Singh, PhD is an Assistant Professor in the Department of Computer Science and Engineering at Pandit Deendayal Energy University, Gandhinagar, Gujarat. He has published various articles in SCI journals and international conferences of repute. His primary areas of interest include artificial intelligence, machine learning, deep learning, computational biology, and drug discovery.

Amit Kant Pandit, PhD is a Professor in the Department of Electronics and Computer Engineering at Shri Mata Vaishno Devi University, Katra, India with more than 26 years of experience. He has authored and co-authored more than 80 publications, including research papers in peer-­reviewed journals and conferences, and two Indian and one Australian patent. He specializes in artificial intelligence and image processing.

Paras Chawla, PhD is a distinguished academic leader, researcher, and innovator with more than 24 years of experience spanning academia, research, industry, and administration. He serves as a Professor and Director in the Amity School of Engineering and Technology at the Amity Institute of Information Technology. He has published more than 120 research papers and holds 24 patents, 15 of which have been granted. His expertise is in AI, machine learning, data science, 5G communications, computer vision, and smart technologies.